GO: A two-step generative optimization method for point cloud registration.

Abstract

Point cloud registration aligns point clouds through transformation or deformation. The existing advanced registration methods perform poorly in registering under various perturbations, especially for large rotations and small overlaps. Moreover, these methods cannot achieve rigid and non-rigid registrations simultaneously. In this paper, we propose a two-step generative optimization (GO) method to achieve registration, which alleviates the constraints of transformation representation and completely circumvents the matching of correspondences, making it suitable for rigid registration with large rotations and small overlaps, as well as for non-rigid registration. GO achieves registration by generating a pseudo point cloud approximating the target point cloud. The pseudo point cloud is iteratively deformed to approach the target point cloud through an estimation step (ES) and an updating step (US). The ES step generates initial pseudo point clouds in three different views and estimates their deformation via the extracted features decoding the similarity between the pseudo point clouds and the target point cloud. The US step calculates regressors to update the current estimations, generating new pseudo point clouds closer to the target point cloud. The final pseudo point clouds generated are averaged to attain the registration result. We evaluate the performance of GO for rigid and non-rigid registrations through the comparison with seven deep learning-based methods on eight datasets, consisting of both 3D and 2D data and ranging from synthetic to real scenes. Our experimental results demonstrate the high robustness and stability of GO in rigid registrations and illustrate the feasibility and high accuracy for non-rigid registrations.